绘制集成树在鸢尾花数据集上的决策面#
绘制在鸢尾花数据集的特征对上训练的随机树森林的决策面。
此图比较了由决策树分类器（第一列）、随机森林分类器（第二列）、极端树分类器（第三列）和 AdaBoost 分类器（第四列）学习到的决策面。
在第一行中，分类器仅使用萼片宽度和萼片长度特征构建，在第二行中仅使用花瓣长度和萼片长度构建，在第三行中仅使用花瓣宽度和花瓣长度构建。
按质量降序排列，当（在本示例之外）使用所有 4 个特征、30 个估计器进行训练并使用 10 折交叉验证进行评分时，我们看到
ExtraTreesClassifier() # 0.95 score
RandomForestClassifier() # 0.94 score
AdaBoost(DecisionTree(max_depth=3)) # 0.94 score
DecisionTree(max_depth=None) # 0.94 score
增加 AdaBoost 的 max_depth 会降低分数的标准差（但平均分数不会提高）。
有关每个模型的更多详细信息，请参阅控制台的输出。
在本例中，您可以尝试
更改
DecisionTreeClassifier和AdaBoostClassifier的max_depth，也许可以尝试为DecisionTreeClassifier设置max_depth=3，或为AdaBoostClassifier设置max_depth=None更改
n_estimators
值得注意的是，RandomForests 和 ExtraTrees 可以在多个内核上并行拟合，因为每棵树都是独立于其他树构建的。AdaBoost 的样本是按顺序构建的，因此不使用多个内核。
DecisionTree with features [0, 1] has a score of 0.9266666666666666
RandomForest with 30 estimators with features [0, 1] has a score of 0.9266666666666666
ExtraTrees with 30 estimators with features [0, 1] has a score of 0.9266666666666666
AdaBoost with 30 estimators with features [0, 1] has a score of 0.82
DecisionTree with features [0, 2] has a score of 0.9933333333333333
RandomForest with 30 estimators with features [0, 2] has a score of 0.9933333333333333
ExtraTrees with 30 estimators with features [0, 2] has a score of 0.9933333333333333
AdaBoost with 30 estimators with features [0, 2] has a score of 0.9933333333333333
DecisionTree with features [2, 3] has a score of 0.9933333333333333
RandomForest with 30 estimators with features [2, 3] has a score of 0.9933333333333333
ExtraTrees with 30 estimators with features [2, 3] has a score of 0.9933333333333333
AdaBoost with 30 estimators with features [2, 3] has a score of 0.9866666666666667
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.colors import ListedColormap
from sklearn.datasets import load_iris
from sklearn.ensemble import (
AdaBoostClassifier,
ExtraTreesClassifier,
RandomForestClassifier,
)
from sklearn.tree import DecisionTreeClassifier
# Parameters
n_classes = 3
n_estimators = 30
cmap = plt.cm.RdYlBu
plot_step = 0.02 # fine step width for decision surface contours
plot_step_coarser = 0.5 # step widths for coarse classifier guesses
RANDOM_SEED = 13 # fix the seed on each iteration
# Load data
iris = load_iris()
plot_idx = 1
models = [
DecisionTreeClassifier(max_depth=None),
RandomForestClassifier(n_estimators=n_estimators),
ExtraTreesClassifier(n_estimators=n_estimators),
AdaBoostClassifier(
DecisionTreeClassifier(max_depth=3),
n_estimators=n_estimators,
algorithm="SAMME",
),
]
for pair in ([0, 1], [0, 2], [2, 3]):
for model in models:
# We only take the two corresponding features
X = iris.data[:, pair]
y = iris.target
# Shuffle
idx = np.arange(X.shape[0])
np.random.seed(RANDOM_SEED)
np.random.shuffle(idx)
X = X[idx]
y = y[idx]
# Standardize
mean = X.mean(axis=0)
std = X.std(axis=0)
X = (X - mean) / std
# Train
model.fit(X, y)
scores = model.score(X, y)
# Create a title for each column and the console by using str() and
# slicing away useless parts of the string
model_title = str(type(model)).split(".")[-1][:-2][: -len("Classifier")]
model_details = model_title
if hasattr(model, "estimators_"):
model_details += " with {} estimators".format(len(model.estimators_))
print(model_details + " with features", pair, "has a score of", scores)
plt.subplot(3, 4, plot_idx)
if plot_idx <= len(models):
# Add a title at the top of each column
plt.title(model_title, fontsize=9)
# Now plot the decision boundary using a fine mesh as input to a
# filled contour plot
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx, yy = np.meshgrid(
np.arange(x_min, x_max, plot_step), np.arange(y_min, y_max, plot_step)
)
# Plot either a single DecisionTreeClassifier or alpha blend the
# decision surfaces of the ensemble of classifiers
if isinstance(model, DecisionTreeClassifier):
Z = model.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
cs = plt.contourf(xx, yy, Z, cmap=cmap)
else:
# Choose alpha blend level with respect to the number
# of estimators
# that are in use (noting that AdaBoost can use fewer estimators
# than its maximum if it achieves a good enough fit early on)
estimator_alpha = 1.0 / len(model.estimators_)
for tree in model.estimators_:
Z = tree.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
cs = plt.contourf(xx, yy, Z, alpha=estimator_alpha, cmap=cmap)
# Build a coarser grid to plot a set of ensemble classifications
# to show how these are different to what we see in the decision
# surfaces. These points are regularly space and do not have a
# black outline
xx_coarser, yy_coarser = np.meshgrid(
np.arange(x_min, x_max, plot_step_coarser),
np.arange(y_min, y_max, plot_step_coarser),
)
Z_points_coarser = model.predict(
np.c_[xx_coarser.ravel(), yy_coarser.ravel()]
).reshape(xx_coarser.shape)
cs_points = plt.scatter(
xx_coarser,
yy_coarser,
s=15,
c=Z_points_coarser,
cmap=cmap,
edgecolors="none",
)
# Plot the training points, these are clustered together and have a
# black outline
plt.scatter(
X[:, 0],
X[:, 1],
c=y,
cmap=ListedColormap(["r", "y", "b"]),
edgecolor="k",
s=20,
)
plot_idx += 1 # move on to the next plot in sequence
plt.suptitle("Classifiers on feature subsets of the Iris dataset", fontsize=12)
plt.axis("tight")
plt.tight_layout(h_pad=0.2, w_pad=0.2, pad=2.5)
plt.show()
脚本总运行时间：（0 分 8.308 秒）
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